閱讀tensorflow

Question

SVHN數據集我努力訓練街景門牌號（SVHN）數據在本教程中（Convolutional Neural Networks） 我用

scipy.io.loadmat

，但它不工作，並給了我這個錯誤::

TypeError: Expected string, got {'header': b'MATLAB 5.0 MAT-file, Platform: GLNXA64, Created on: Mon Dec 5 21:09:26 2011', 'version': '1.0', 'globals': [], 'X': array([[[[ 33, 84, 19, ..., 92, 190, 216], [ 30, 76, 54, ..., 78, 188, 217], [ 38, 59, 110, ..., 101, 191, 212]],

[[ 15, 86, 20, ..., 94, 205, 221], 
    [ 23, 73, 52, ..., 82, 203, 222], 
    [ 19, 66, 111, ..., 105, 206, 217]], 

    [[ 15, 77, 25, ..., 114, 220, 226], 
    [ 17, 78, 57, ..., 101, 218, 227], 
    [ 19, 56, 116, ..., 125, 220, 221]], 

    ..., 
    [[ 72, 90, 65, ..., 200, 229, 200], 
    [ 65, 78, 144, ..., 201, 231, 199], 
    [ 56, 69, 223, ..., 203, 224, 191]], 

    [[ 82, 88, 78, ..., 192, 229, 193], 
    [ 77, 77, 148, ..., 193, 229, 188], 
    [ 57, 67, 218, ..., 195, 224, 182]], 

    [[ 89, 88, 98, ..., 190, 229, 197], 
    [ 79, 78, 158, ..., 191, 228, 189], 
    [ 59, 66, 220, ..., 193, 223, 186]]], 


    [[[ 28, 85, 21, ..., 92, 183, 204], 
    [ 39, 77, 53, ..., 78, 182, 205], 
    [ 35, 61, 110, ..., 103, 186, 202]], 

    [[ 14, 83, 19, ..., 93, 200, 210], 
    [ 25, 73, 52, ..., 80, 199, 211], 
    [ 22, 64, 106, ..., 106, 201, 208]], 

    [[ 14, 74, 25, ..., 111, 218, 220], 
    [ 20, 69, 56, ..., 98, 217, 221], 
    [ 17, 59, 111, ..., 124, 218, 217]], 

    ..., 
    [[ 40, 89, 63, ..., 181, 227, 201], 
    [ 39, 82, 137, ..., 180, 228, 199], 
    [ 50, 64, 208, ..., 184, 223, 193]], 

    [[ 67, 88, 91, ..., 177, 227, 195], 
    [ 58, 79, 153, ..., 176, 226, 191], 
    [ 52, 70, 214, ..., 180, 222, 186]], 

    [[ 83, 88, 130, ..., 183, 228, 196], 
    [ 78, 81, 180, ..., 182, 224, 190], 
    [ 60, 67, 229, ..., 187, 221, 186]]], 


    [[[ 40, 83, 21, ..., 99, 171, 198], 
    [ 41, 76, 53, ..., 84, 170, 198], 
    [ 38, 60, 110, ..., 112, 175, 197]], 

    [[ 18, 78, 20, ..., 94, 189, 202], 
    [ 21, 77, 51, ..., 81, 189, 202], 
    [ 26, 58, 106, ..., 110, 193, 201]], 

    [[ 16, 61, 22, ..., 107, 213, 212], 
    [ 17, 50, 52, ..., 94, 213, 211], 
    [ 23, 54, 106, ..., 123, 215, 210]], 

    ..., 
    [[ 23, 90, 79, ..., 167, 231, 203], 
    [ 29, 85, 147, ..., 166, 230, 200], 
    [ 45, 63, 210, ..., 171, 226, 196]], 

    [[ 35, 88, 125, ..., 172, 229, 198], 
    [ 42, 83, 181, ..., 171, 226, 194], 
    [ 44, 66, 230, ..., 176, 223, 191]], 

    [[ 72, 85, 178, ..., 185, 227, 195], 
    [ 69, 82, 218, ..., 184, 223, 190], 
    [ 53, 70, 254, ..., 189, 220, 187]]], 


    ..., 
    [[[ 86, 100, 88, ..., 99, 187, 233], 
    [ 81, 98, 162, ..., 94, 185, 226], 
    [ 75, 72, 237, ..., 110, 186, 228]], 

    [[ 87, 98, 89, ..., 96, 204, 230], 
    [ 82, 94, 163, ..., 91, 202, 224], 
    [ 71, 76, 238, ..., 109, 199, 225]], 

    [[ 82, 95, 84, ..., 108, 217, 228], 
    [ 79, 93, 156, ..., 103, 217, 223], 
    [ 65, 73, 230, ..., 124, 210, 221]], 

    ..., 
    [[104, 104, 62, ..., 210, 204, 198], 
    [104, 104, 142, ..., 207, 200, 196], 
    [ 87, 86, 227, ..., 204, 195, 190]], 

    [[104, 102, 67, ..., 206, 196, 184], 
    [105, 102, 144, ..., 202, 193, 183], 
    [ 81, 87, 226, ..., 200, 189, 177]], 

    [[103, 100, 74, ..., 203, 196, 189], 
    [105, 101, 145, ..., 197, 193, 187], 
    [ 78, 78, 225, ..., 199, 189, 182]]], 


    [[[ 84, 103, 88, ..., 94, 186, 231], 
    [ 86, 104, 164, ..., 91, 184, 226], 
    [ 64, 79, 240, ..., 103, 185, 228]], 

    [[ 86, 106, 87, ..., 94, 198, 229], 
    [ 79, 104, 160, ..., 91, 197, 224], 
    [ 72, 79, 237, ..., 104, 194, 225]], 

    [[ 82, 103, 88, ..., 110, 211, 227], 
    [ 76, 103, 159, ..., 107, 211, 223], 
    [ 72, 87, 237, ..., 121, 204, 222]], 

    ..., 
    [[110, 103, 60, ..., 219, 222, 195], 
    [103, 104, 141, ..., 218, 216, 194], 
    [ 84, 86, 230, ..., 215, 212, 186]], 

    [[106, 103, 61, ..., 218, 214, 181], 
    [105, 103, 141, ..., 215, 209, 181], 
    [ 85, 87, 228, ..., 212, 205, 173]], 

    [[106, 105, 65, ..., 212, 208, 186], 
    [104, 99, 143, ..., 209, 205, 183], 
    [ 86, 81, 226, ..., 209, 200, 177]]], 


    [[[ 85, 103, 84, ..., 88, 190, 230], 
    [ 88, 106, 160, ..., 87, 188, 226], 
    [ 68, 82, 238, ..., 94, 190, 227]], 

    [[ 89, 103, 81, ..., 85, 199, 230], 
    [ 82, 105, 154, ..., 84, 197, 226], 
    [ 72, 87, 233, ..., 93, 194, 227]], 

    [[ 85, 104, 87, ..., 105, 208, 229], 
    [ 79, 106, 158, ..., 103, 208, 225], 
    [ 67, 91, 238, ..., 114, 201, 226]], 

    ..., 
    [[111, 113, 63, ..., 217, 232, 190], 
    [104, 103, 144, ..., 217, 227, 190], 
    [ 87, 88, 235, ..., 214, 223, 181]], 

    [[109, 104, 62, ..., 221, 226, 178], 
    [105, 104, 143, ..., 220, 221, 177], 
    [ 86, 88, 232, ..., 219, 216, 169]], 

    [[103, 103, 63, ..., 218, 218, 181], 
    [106, 98, 145, ..., 217, 213, 178], 
    [ 79, 80, 231, ..., 218, 209, 171]]]], dtype=uint8), 'y': array([[1], 
    [9], 
    [2], 
    ..., 
    [1], 
    [6], 
    [9]], dtype=uint8)} of type 'dict' instead. 

我無法理解的問題，該如何解決。

Answer 1

該教程使用從文件中讀取數據的方法，這對於數據集太大而不能保留在內存中的情況是很典型的。在本教程的這一部分中有幾個步驟，主要在cifar10_input.py中實施，他們通常遵循步驟here。

cifar10_input.py獲取從中讀取數據的文件名字符串列表。沒有看到更多的代碼，我猜這就是爲什麼你有這個特定的錯誤。

你要麼需要確保SVHN .mat文件具有正確預期的二進制格式（我懷疑是這種情況），然後交換該文件名中cifar10_input.py爲cifar10二進制文件，或重寫cifar10_train.py使用佔位符feed_dict參數代替讀取管道。請記住，SVHN將需要被縮減爲與cifar圖像在cifar10_input.py中相同的尺寸。另外，對於feed_dict方法，SVHN數據集可能太大。